Fall 2019 - CMPT 353 D100

Computational Data Science (3)

Class Number: 8986

Delivery Method: In Person

Overview

  • Course Times + Location:

    Tu 1:30 PM – 2:20 PM
    SSCC 9001, Burnaby

    Th 12:30 PM – 2:20 PM
    SWH 10041, Burnaby

  • Exam Times + Location:

    Dec 10, 2019
    12:00 PM – 3:00 PM
    SSCB 9200, Burnaby

  • Prerequisites:

    CMPT 225 and (STAT 101, STAT 270, BUEC 232, ENSC 280, or MSE 210).

Description

CALENDAR DESCRIPTION:

Basic concepts and programming tools for handling and processing data. Includes data acquisition, cleaning data sources, application of machine learning techniques and data analysis techniques, large-scale computation on a computing cluster.

COURSE DETAILS:

This course will be an introduction to the tools and techniques in data science. We will explore common challenges and solutions used in analysis of data.

Topics

  • Basics of data science: concepts, goals, motivation, expectations.
  • Introduction to selected data processing tools: Python with numpy and pandas.
  • Working with data. Cleaning data; extract, transform, load tasks; applying concepts from statistics.
  • Machine learning basics with existing implementations (such as scikit-learn).
  • Data analysis strategies: selecting techniques from statistics and machine learning.
  • Big data tools.
  • Data visualization and summarizing results.

Grading

NOTES:

Details to be announced in first week of class. Will include weekly exercises, a project, quizzes, and exams.

Students must attain an overall passing grade on the weighted average of exams in the course in order to obtain a clear pass (C- or better).

Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS